Derin Öğrenme Nesne Tespit Teknikleri Kullanılarak Serebral Damar Görüntülerinde Dal ve Uç Noktalarının Tespiti

Abstract

Bu çalışmada, derin öğrenme tabanlı nesne algılama tekniklerini kullanarak iki boyutlu beyin damarı görüntülerinde dallanma ve uç noktaları tespit etmek için son teknoloji bir metodoloji sunuyoruz. Geleneksel görüntü işleme yöntemleri uygulanabilir alternatifler olsa da, derin öğrenmeyi benimsememiz doğruluk ve verimlilikte kayda değer ilerleme sergilemekteyiz. Laboratuvar ortamlarından elde edilen ham veri setinin titizlikle temizlenmesi ve etiketlenmesinin ardından, hem eğitim hem de test aşamaları için derin öğrenme algoritmalarıyla uyumluluğu sağlamak üzere COCO formatına dönüştürüyoruz. Ardından Detectron2 çerçevesi içinde Fast R-CNN, Faster R-CNN, RetinaNet ve RPN olmak üzere dört derin öğrenme nesne algılama yöntemini kullandığımız çalışmamız dikkate değer sonuçlar elde etmektedir. Birleşme üzerinden kesişim (intersection over union) yöntemi kullanılarak yapılan değerlendirme, derin öğrenme yaklaşımımızın sağlam performansının altını çiziyor ve %90'ı aşan bir başarı oranına sahip olduğunu gösteriyoruz. Çalışma sadece nörogörüntüleme analizini geliştirmekle kalmıyor, aynı zamanda nörovasküler çalışmalarda teşhis ve araştırma uygulamalarını kolaylaştırma konusunda büyük bir potansiyele sahiptir.

Keywords

• Nesne Tespiti
• Derin Öğrenme
• Dallanma ve Uç Nokta Tespiti
• Beyin Damar Analizi

Branch and End Points Detection in Cerebral Vessels Images Using Deep Learning Object Detection Techniques

Abstract

In this study, we introduce a cutting-edge methodology for detecting branching and endpoints in two-dimensional brain vessel images, employing deep learning-based object detection techniques. While conventional image processing methods are viable alternatives, our adoption of deep learning showcases notable advancements in accuracy and efficiency. Following meticulous cleaning and labeling of the raw dataset sourced from laboratory environments, we meticulously convert it into the COCO format, ensuring compatibility with deep learning algorithms for both training and testing phases. Utilizing four deep learning object detection methods: fast R-CNN, faster R-CNN, RetinaNet and RPN within the Detectron2 framework, our study achieves remarkable results. Evaluation using the intersection over union (IoU) method underscores the robust performance of our deep learning approach, boasting a success rate surpassing 90%. This breakthrough not only enhances neuroimaging analysis but also holds immense potential for revolutionizing diagnostic and research practices in neurovascular studies.

Keywords

• Object Detection
• Deep Learning
• Branch and End Point Detection.
• Brain Vessel Analysis

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